1 research outputs found
Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learning
Monitoring of disasters is crucial for mitigating their effects on the
environment and human population, and can be facilitated by the use of unmanned
aerial vehicles (UAV), equipped with camera sensors that produce aerial photos
of the areas of interest. A modern technique for recognition of events based on
aerial photos is deep learning. In this paper, we present the state of the art
work related to the use of deep learning techniques for disaster
identification. We demonstrate the potential of this technique in identifying
disasters with high accuracy, by means of a relatively simple deep learning
model. Based on a dataset of 544 images (containing disaster images such as
fires, earthquakes, collapsed buildings, tsunami and flooding, as well as
non-disaster scenes), our results show an accuracy of 91% achieved, indicating
that deep learning, combined with UAV equipped with camera sensors, have the
potential to predict disasters with high accuracy.Comment: Disaster Management for Resilience and Public Safety Workshop, Proc.
of EnviroInfo 201